A searchable list of some of my publications is below. You can also access my publications from the following sites.
My ORCID is
https://orcid.org/0000-0002-6236-2969
Publications:
1.
M. Brand, I. Essa
Causal Analysis for Visual Gesture Understanding Proceedings Article
In: AAAI Fall Symposium on Computational Models for Integrating Language and Vision, 1995.
@inproceedings{1995-Brand-CAVGU,
title = {Causal Analysis for Visual Gesture Understanding},
author = {M. Brand and I. Essa},
year = {1995},
date = {1995-10-01},
booktitle = {AAAI Fall Symposium on Computational Models for Integrating Language and Vision},
abstract = {We are exploring the use of high-level knowledge about bodies in the visual understanding of gesture. Our hypothesis is that many gestures are metaphorically derived from the motor programs of our everyday interactions with objects and people. For example, many dismissive gestures look like an imaginary object is being brushed or tossed away. At the discourse level, this implicit mass represents a referent in the conversation; at the scene-formation level, the dismissive gesture obeys many of the kinematic and dynamic constraints that would shape an actual tossing. Thus this metaphor provides us with constraints for both discourse annotation and visual processing. In this paper we present some preliminary results interpreting complex gesture sequences in video.},
keywords = {gesture recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
We are exploring the use of high-level knowledge about bodies in the visual understanding of gesture. Our hypothesis is that many gestures are metaphorically derived from the motor programs of our everyday interactions with objects and people. For example, many dismissive gestures look like an imaginary object is being brushed or tossed away. At the discourse level, this implicit mass represents a referent in the conversation; at the scene-formation level, the dismissive gesture obeys many of the kinematic and dynamic constraints that would shape an actual tossing. Thus this metaphor provides us with constraints for both discourse annotation and visual processing. In this paper we present some preliminary results interpreting complex gesture sequences in video.
2.
T. Darrell, I. Essa, A. Pentland
Correlation and Interpolation Networks for Real-Time Expression Analysis/Synthesis Proceedings Article
In: Tesauro, G., Touretzky, D. S., Leen, T. K. (Ed.): Advances in Neural Information Processing Systems (NeurIPS), MIT Press, 1994.
@inproceedings{1994-Darrell-CINREA,
title = {Correlation and Interpolation Networks for Real-Time Expression Analysis/Synthesis},
author = {T. Darrell and I. Essa and A. Pentland},
editor = {G. Tesauro and D. S. Touretzky and T. K. Leen},
url = {https://papers.nips.cc/paper/999-correlation-and-interpolation-networks-for-real-time-expression-analysissynthesis},
year = {1994},
date = {1994-12-01},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
volume = {7},
publisher = {MIT Press},
abstract = {We describe a framework for real-time tracking of facial expressions
that uses neurally-inspired correlation and interpolation methods. A
distributed view-based representation is used to characterize facial state,
and is computed using a replicated correlation network. The ensemble
response of the set of view correlation scores is input to a network based
interpolation method, which maps perceptual state to motor control states
for a simulated 3-D face model. Activation levels of the motor state
correspond to muscle activations in an anatomically derived model. By
integrating fast and robust 2-D processing with 3-D models, we obtain a
system that is able to quickly track and interpret complex facial motions
in real-time.},
keywords = {face & gesture, face processing, gesture recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
We describe a framework for real-time tracking of facial expressions
that uses neurally-inspired correlation and interpolation methods. A
distributed view-based representation is used to characterize facial state,
and is computed using a replicated correlation network. The ensemble
response of the set of view correlation scores is input to a network based
interpolation method, which maps perceptual state to motor control states
for a simulated 3-D face model. Activation levels of the motor state
correspond to muscle activations in an anatomically derived model. By
integrating fast and robust 2-D processing with 3-D models, we obtain a
system that is able to quickly track and interpret complex facial motions
in real-time.
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